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Every year, American high school students take SATs, which are standardized tests intended to measure literacy, numeracy, and writing skills. There are three sections - reading, math, and writing, each with a maximum score of 800 points. These tests are extremely important for students and colleges, as they play a pivotal role in the admissions process.
Analyzing the performance of schools is important for a variety of stakeholders, including policy and education professionals, researchers, government, and even parents considering which school their children should attend.
You have been provided with a dataset called schools.csv, which is previewed below.
You have been tasked with answering three key questions about New York City (NYC) public school SAT performance.
# Re-run this cell
import pandas as pd
# Read in the data
schools = pd.read_csv("schools.csv")
# Preview the data
schools.head()
# Start coding here...
# Add as many cells as you like...#Which NYC schools have the best math results?
#subsetting just the school names and maths results
all_math_results = schools[['school_name', 'average_math']]
math.head()
#subsetting for the schools with the best maths result
best_math_schools = all_math_results[all_math_results['average_math'] > 640]
best_math_schools = best_math_schools.sort_values(by = 'average_math', ascending=False)
print(best_math_schools)#What are the top 10 performing schools based on the combined SAT scores?
#subsetting data for school names and their avg maths, reading and writing scores
filtered_df = schools[['school_name','average_math','average_reading','average_writing']]
#extracting the avg maths, reading and writing values as a list
average_math = schools['average_math']
average_reading = schools['average_reading']
average_writing = schools['average_writing']
#summing values as a list and adding as a brand new column to my filtered DataFrame
total_SAT = average_math + average_reading + average_writing
filtered_df['total_SAT'] = total_SAT
#sorting values from best to worst and taking only the 10 best schools
sorted_filtered_df = filtered_df.sort_values(by = 'total_SAT', ascending=False)
top_10_schools = sorted_filtered_df[0:10]
#only filtering the "top_10_schools" for just the school name and the total_SAT score
top_10_schools = top_10_schools[['school_name', 'total_SAT']]
print(top_10_schools)#Which single borough has the largest standard deviation in the combined SAT score?
#firstly, we must append the original schools table with the combined SAT scores
total_SAT = average_math + average_reading + average_writing
schools['total_SAT'] = total_SAT
schools.head()#let's group values by borough and apply std aggregation function on the total_SAT column
std = schools.groupby('borough')['total_SAT'].std().sort_values(ascending = False)
print(std)num_schools = len( schools[ schools["borough"] == "Manhattan"] )
print(num_schools)
borough_total_SAT_mean = schools.groupby('borough')['total_SAT'].mean()
average_SAT = borough_total_SAT_mean['Manhattan']
average_SAT= round(average_SAT, 2)
print(average_SAT)
borough_total_SAT_std = schools.groupby('borough')['total_SAT'].std()
std_SAT = borough_total_SAT_std['Manhattan']
std_SAT = round(std_SAT, 2)
print(std_SAT)
data = [
['Manhattan', num_schools, average_SAT, std_SAT]
]
largest_std_dev = pd.DataFrame(data, columns=['borough', 'num_schools', 'average_SAT', 'std_SAT'])
print(largest_std_dev)